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Sabrina K. Schaupp

dblp:425/3228 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 67% Medical and health informatics · 33%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
consensus clustering
0.812024
longmixr: a tool for robust clustering of high-dimensional cross-sectional and longitudinal variables of mixed data types · Bioinform. 2024
Medical and health informatics › precision medicine
patient stratification
0.812024
longmixr: a tool for robust clustering of high-dimensional cross-sectional and longitudinal variables of mixed data types · Bioinform. 2024
Bioinformatics and computational biology
statistical genetics
0.812024
longmixr: a tool for robust clustering of high-dimensional cross-sectional and longitudinal variables of mixed data types · Bioinform. 2024

Methods — techniques the papers use, named apart from their topics

finite mixture modeling · 0.8consensus clustering · 0.8
YearPublicationVenuePosition
2024 longmixr: a tool for robust clustering of high-dimensional cross-sectional and longitudinal variables of mixed data types
abstract
SUMMARY: Accurate clustering of mixed data, encompassing binary, categorical, and continuous variables, is vital for effective patient stratification in clinical questionnaire analysis. To address this need, we present longmixr, a comprehensive R package providing a robust framework for clustering mixed longitudinal data using finite mixture modeling techniques. By incorporating consensus clustering, longmixr ensures reliable and stable clustering results. Moreover, the package includes a detailed vignette that facilitates cluster exploration and visualization. AVAILABILITY AND IMPLEMENTATION: The R package is freely available at https://cran.r-project.org/package=longmixr with detailed documentation, including a case vignette, at https://cellmapslab.github.io/longmixr/.
Jonas Hagenberg, Monika Budde, Teodora Pandeva, Ivan Kondofersky, Sabrina K. Schaupp, Fabian J. Theis, Thomas G. Schulze, Nikola S. Müller, Urs Heilbronner, Richa Batra, Janine Knauer-Arloth
Bioinform.5